Using FusionCharts with C# (ASP.NET) > Basic Examples |
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In this section we will build up simple charts using ASP.NET. We'll cover the following examples here:
Let's quickly see each of them. |
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| All code discussed here is present in Download Package > Code > VB_NET > BasicExample folder. | ||||||||||||||||||
| Setting up the charts for use | ||||||||||||||||||
| In our code, we've used the charts contained in Download Package > Code > VB_NET > FusionCharts folder. When you run your samples, you need to make sure that the SWF files are in proper location. | ||||||||||||||||||
| Plotting a chart from data contained in Data.xml | ||||||||||||||||||
Let's now get to building our first example. In this example, we'll create a "Monthly Unit Sales" chart using dataURL method. For a start, we'll hard code our XML data in a physical XML document Data.xml and then utilize it in our chart contained in an ASP.NET Page (BasicChart.aspx). Let's first have a look at the XML Data document: |
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| <graph caption='Monthly Unit Sales' xAxisName='Month' yAxisName='Units' decimalPrecision='0' formatNumberScale='0'> <set name='Jan' value='462' color='AFD8F8' /> <set name='Feb' value='857' color='F6BD0F' /> <set name='Mar' value='671' color='8BBA00' /> <set name='Apr' value='494' color='FF8E46' /> <set name='May' value='761' color='008E8E' /> <set name='Jun' value='960' color='D64646' /> <set name='Jul' value='629' color='8E468E' /> <set name='Aug' value='622' color='588526' /> <set name='Sep' value='376' color='B3AA00' /> <set name='Oct' value='494' color='008ED6' /> <set name='Nov' value='761' color='9D080D' /> <set name='Dec' value='960' color='A186BE' /> </graph> |
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This XML is stored as Data.xml in Data Folder under BasicExample folder. It basically contains the data to create a single series chart to show "Monthly Unit Sales". We'll plot this on a Column 3D Chart. Let's see how to do that. To plot a Chart that consumes this data, you need to include the HTML code to embed a Flash object and then provide the requisite parameters. To make things simpler for you, we've put all this functionality in InfoSoftGlobal.FusionCharts class inside the dll file FusionCharts.dll. This dll file is contained in Download Package > Code > VB_NET > bin. So, whenever you need to work with FusionCharts in ASP.NET, just use the defined functions in the InfoSoftGlobal.FusionCharts class and then you can work with FusionCharts very easily. Let's see it in example. BasicChart.aspx contains the following code to render the chart: |
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<%@ Page Language="C#" %> |
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| Here, we're basically rendering the chart by RenderChartHtml() function. In this code, we've just used the FusionCharts class, which is contained within InfoSoftGlobal namepspace of the dll file, and called the RenderChartHTML method to return the pertinent HTML output for the chart. To use this function, you can pass the following parameters (in same order): | ||||||||||||||||||
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When you now run this page, you'll see a chart like the one below. If you do not see a chart like the one below, please follow the steps listed in Debugging your Charts > Basic Troubleshooting section of this documentation. |
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| So, you just saw how simple it is to create a chart using ASP.NET and FusionCharts. Let's now convert the above chart to use dataXML method. | ||||||||||||||||||
| Changing the above chart into a single page chart using dataXML method | ||||||||||||||||||
| To convert this chart to use dataXML method, we create another page BasicDataXML.aspx in the same folder with following code: | ||||||||||||||||||
<%@ Page Language="C#" AutoEventWireup="false" CodeFile="BasicDataXML.aspx.cs" Inherits="BasicExample_BasicDataXML" %> |
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| Again, as you can see, in this page, we're just calling the CreateCharts() function, which is defined in the code-behind page. The code-behind page i.e., BasicDataXML.aspx.cs contains the following code: | ||||||||||||||||||
using System; public partial class BasicExample_BasicDataXML : System.Web.UI.Page //This page demonstrates the ease of generating charts using FusionCharts. //Ideally, you would generate XML data documents at run-time, after interfacing with //Create an XML data document in a string variable string strXML; //Create the chart - Column 3D Chart with data from strXML variable using dataXML method } |
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Here,
When you see this chart, you'll get the same results as before. |
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| Using FusionCharts JavaScript class to embed the chart. | ||||||||||||||||||
| If you see the charts from previous examples in the latest versions of Internet Explorer, you'll see a screen as below: | ||||||||||||||||||
Internet Explorer asks you to "Click and activate..." to use the chart. This is happening because of a technical issue on behalf of Microsoft. As such, all Flash movies need to be clicked once before you can start interacting with them. However, the good news is that there's a solution to it. This thing happens only when you directly embed the HTML code of the chart. It would NOT happen when you use JavaScript to embed the chart. To see how to embed using JavaScript at code level, please see Creating Your First Chart > JavaScript Embedding Section. Again, to make things simpler for you, we've provided an ASP.NET function called RenderChart() which helps you wrap this JavaScript function in ASP.NET function, so that you don't have to get your hands dirty with JavaScript, Flash and HTML. This function is contained in the previously used InfoSoftGlobal.FusionCharts class. Let's now quickly put up a sample to show the use of this function. We create
another ASP.NET page SimpleChart.aspx to use this function to plot a chart from data contained in our previously
created Data.xml file. Like before, SimpleChart.aspx contains the following code: |
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| <%@ Page Language="C#" %> <%@ Import Namespace="InfoSoftGlobal" %> <HTML> <HEAD> <TITLE> FusionCharts Free - Simple Column 3D Chart </TITLE> <% //You need to include the following JS file, if you intend to embed the chart using JavaScript. %> <SCRIPT LANGUAGE="Javascript" SRC="../FusionCharts/FusionCharts.js"></SCRIPT> </HEAD> <BODY> <% //Create the chart - Column 3D Chart with data from Data/Data.xml Response.Write(FusionCharts.RenderChart("../FusionCharts/FCF_Column3D.swf", "Data/Data.xml", "", "myFirst", "600", "300", False, False)) %> </BODY> </HTML> |
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In the above code, we've:
|
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RenderChart() method is contained in InfoSoftGlobal.FusionCharts class; it returns the HTML code for the chart (using JavaScript embedding). Since we're using a physical Data.xml file here, we've used the dataURL method to provide the path of the file. The RenderChart() method takes in the following parameters: |
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|
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| When you now view the chart, you'll see that no activation is required even in Internet Explorer. | ||||||||||||||||||


simple example
block 1
block 2
example with button and step of 2
Next
block 1
block 2
block 3
example with non looping list and multiple externals triggers
block 1
block 2
prev
next


Greetings, I’m Arief Budi Prasetyo.
I’m a web developer living in Sragen, Jawa Tengah, Indonesia. I am a fan of technology, music, and entrepreneurship. I’m also interested in programming and video games. You can book time with me with a click on the button bellow.
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Copyright © 2020 archytech. All rights reserved. Template Design from Bootstrap


This well-known stop on the crusing route is an interesting town to visit.
Position 10º 12' 24" N, 64º 40' 05" W.
The marinas in the El Morro Tourist complex offer help with clearance formalities. All of the marinas have 24 security and safety here is reported to be the best in all of Venezuela.
On departure, the Port Captain requires you to file a sail plan before being allowed to leave a Marina.
Last updated August 2012.
The marinas are the only safe place to berth here. There are several to choose from.
Anchoring is not advisable for safety reasons.
Due to several fatal boating accidents in the El Morro Lagoon area, the local authorities have implemented a number of regulations to be followed if using a dinghy in this area. The Costa Guardia may stop and fine you if you are not compliant. All occupants of dinghies should be wearing life preservers, and proof of ownership of the dinghy and outboard should be on board. Ask for the latest copy of the rules on arrival as they are subject to change.
Last updated August 2012.
Note October 2014: The Caribbean Safety and Security Net reported a burglary from a yacht left unattended at Bahia Redonda Marina.


Finding_Condition Class Reference
| Inherits from | NSObject |
| Conforms to | PicoBindable |
| Declared in | Finding_Condition.h Finding_Condition.m |
Tasks
-
conditionId
The numeric ID (e.g., 1000) for the item condition.
property
In item results, this is only returned when the seller listed the
item with a condition ID.
Some categories don’t support or require condition IDs
(e.g., most Antiques categories don’t). Also, until spring 2011,
some GTC listings may define the item condition in item specifics
instead, so no ID is returned.
If you specify Condition in itemFilter, the response returns items
with the correctly matching condition(s), even if conditionId is
not returned. For example, if you specify a value of “New"
or "1000” in the item filter, the response only returns new items. -
conditionDisplayName
The human-readable label for the item condition.
property
Display names are localized for the site on which they’re listed
(not necessarily the site on which they’re viewed).
In item results, this is only returned when the seller specified the item’s condition
using a structured format eBay recognizes (e.g., conditionId or an
older item specifics format).
When conditionId is also present:
Most categories use the same display name for the same
condition ID. Some categories may override the display name based
on buyer expectations for items in the category.
For example, condition ID 1000 could be called “New"
in one category and "New with tags” in another.
If an item is listed in two categories, the primary category
controls the display name.
Behind the scenes, eBay’s search engine uses the ID (not the
display name) to determine whether items are new, used, or
refurbished. So, if you need to normalize the conditions
across categories (such as to group items by condition),
it may be easier to use the ID and then show the varying
display names for reference.
In condition histograms: If you search against
a specific category and some items match based on their
secondary category, the histogram only shows the display name
if the secondary category supports the condition.
(Condition IDs and names are dependent on the primary category.)
However, the histogram shows the condition ID and item counts.
This should only occur in a very small percent of results.
Histograms may support display names in these cases later in 2011.
As a workaround, you can fill in the missing name based on the
“Item Condition IDs and Names” (link below) or based on
the condition from an applicable item in the results.
For example, suppose a seller lists a concert T-shirt in a
clothing category with the condition “New without tags” (1500),
and also in a music accessories secondary category (where
“New without tags” isn’t a recognized condition).
If you specify the music accessories category in your request,
the condition ID (1500) is shown in the histogram, but not the
display name. However, the display name is shown within the items. -
delimiter
(public property)
property -
any
(public property)
property
Properties
any
(public property)
@property (nonatomic, retain) NSMutableArray *any
Discussion
(public property)
entry type : PicoAnyElement, wrapper for primitive anyelement
Declared In
Finding_Condition.h
conditionDisplayName
The human-readable label for the item condition.
Display names are localized for the site on which they’re listed
(not necessarily the site on which they’re viewed).
In item results, this is only returned when the seller specified the item’s condition
using a structured format eBay recognizes (e.g., conditionId or an
older item specifics format).
When conditionId is also present:
Most categories use the same display name for the same
condition ID. Some categories may override the display name based
on buyer expectations for items in the category.
For example, condition ID 1000 could be called “New"
in one category and "New with tags” in another.
If an item is listed in two categories, the primary category
controls the display name.
Behind the scenes, eBay’s search engine uses the ID (not the
display name) to determine whether items are new, used, or
refurbished. So, if you need to normalize the conditions
across categories (such as to group items by condition),
it may be easier to use the ID and then show the varying
display names for reference.
In condition histograms: If you search against
a specific category and some items match based on their
secondary category, the histogram only shows the display name
if the secondary category supports the condition.
(Condition IDs and names are dependent on the primary category.)
However, the histogram shows the condition ID and item counts.
This should only occur in a very small percent of results.
Histograms may support display names in these cases later in 2011.
As a workaround, you can fill in the missing name based on the
“Item Condition IDs and Names” (link below) or based on
the condition from an applicable item in the results.
For example, suppose a seller lists a concert T-shirt in a
clothing category with the condition “New without tags” (1500),
and also in a music accessories secondary category (where
“New without tags” isn’t a recognized condition).
If you specify the music accessories category in your request,
the condition ID (1500) is shown in the histogram, but not the
display name. However, the display name is shown within the items.
@property (nonatomic, retain) NSString *conditionDisplayName
Discussion
The human-readable label for the item condition.
Display names are localized for the site on which they’re listed
(not necessarily the site on which they’re viewed).
In item results, this is only returned when the seller specified the item’s condition
using a structured format eBay recognizes (e.g., conditionId or an
older item specifics format).
When conditionId is also present:
Most categories use the same display name for the same
condition ID. Some categories may override the display name based
on buyer expectations for items in the category.
For example, condition ID 1000 could be called “New"
in one category and "New with tags” in another.
If an item is listed in two categories, the primary category
controls the display name.
Behind the scenes, eBay’s search engine uses the ID (not the
display name) to determine whether items are new, used, or
refurbished. So, if you need to normalize the conditions
across categories (such as to group items by condition),
it may be easier to use the ID and then show the varying
display names for reference.
In condition histograms: If you search against
a specific category and some items match based on their
secondary category, the histogram only shows the display name
if the secondary category supports the condition.
(Condition IDs and names are dependent on the primary category.)
However, the histogram shows the condition ID and item counts.
This should only occur in a very small percent of results.
Histograms may support display names in these cases later in 2011.
As a workaround, you can fill in the missing name based on the
“Item Condition IDs and Names” (link below) or based on
the condition from an applicable item in the results.
For example, suppose a seller lists a concert T-shirt in a
clothing category with the condition “New without tags” (1500),
and also in a music accessories secondary category (where
“New without tags” isn’t a recognized condition).
If you specify the music accessories category in your request,
the condition ID (1500) is shown in the histogram, but not the
display name. However, the display name is shown within the items.
type : NSString, wrapper for primitive string
Declared In
Finding_Condition.h
conditionId
The numeric ID (e.g., 1000) for the item condition.
In item results, this is only returned when the seller listed the
item with a condition ID.
Some categories don’t support or require condition IDs
(e.g., most Antiques categories don’t). Also, until spring 2011,
some GTC listings may define the item condition in item specifics
instead, so no ID is returned.
If you specify Condition in itemFilter, the response returns items
with the correctly matching condition(s), even if conditionId is
not returned. For example, if you specify a value of “New"
or "1000” in the item filter, the response only returns new items.
@property (nonatomic, retain) NSNumber *conditionId
Discussion
The numeric ID (e.g., 1000) for the item condition.
In item results, this is only returned when the seller listed the
item with a condition ID.
Some categories don’t support or require condition IDs
(e.g., most Antiques categories don’t). Also, until spring 2011,
some GTC listings may define the item condition in item specifics
instead, so no ID is returned.
If you specify Condition in itemFilter, the response returns items
with the correctly matching condition(s), even if conditionId is
not returned. For example, if you specify a value of “New"
or "1000” in the item filter, the response only returns new items.
type : NSNumber, wrapper for primitive int
Declared In
Finding_Condition.h


What steps does a progenitor take to become a specific and fully differentiated neuron?
Organogenesis: "First, you have to make all the components"
Development of the retina
Visual Development Lab
Juan Angueyra
Seth's CV: 30 pages
M.S. and Ph.D.: Solomon Snyder (Johns Hopkins University)
Postdoc: Connie Cepko (Harvard)
Faculty: Johns Hopkins University
Awards: Too many to list
W. M. Keck Foundation Distinguished Young Scholar in Medical Research Award
Klingenstein Fellowship
Basil O’Connor Starter Scholar Award
Sloan Foundation Research Fellowship
Ruth and Milton Steinbach Fund Award for Research in Macular Degeneration
Retina+Vision — Hypothalamus — Sleep
173 peer-reviewed publications (and counting!)
Science vs. Society
Effective use of science communication tools
Early adoption of preprints
Consistency
Peoples
Science vs. Society
Effective use of science communication tools
Early adoption of preprints
Consistency
Peoples
Seth's lab without immigrants
Science vs. Society
Effective use of science communication tools
Early adoption of preprints
Consistency
Peoples
RNAseq duck
Science vs. Society
Effective use of science communication tools
Early adoption of preprints
Consistency → black shirts
Peoples
RNAseq duck
Science vs. Society
Effective use of science communication tools
Early adoption of preprints
Consistency → black shirts
Peoples


National Grid’s Carbon Intensity API provides an indicative trend of regional carbon intensity of the electricity system in Great Britain (GB) up to 2 days ahead of real-time. It provides programmatic and timely access to both forecast and estimated carbon intensity data. The Carbon Intensity forecast includes CO2 emissions related to electricity generation only. This includes emissions from all large metered power stations, interconnector imports, transmission and distribution losses, and accounts for national electricity demand, embedded wind and solar generation.
This page performs queries at the intensity, stats and to-from levels, every two hours, every day. This page does not display results found through the API and focuses solely on time taken to perform queries.
More about the Carbon Intensity (Great Britain) API can be found here.
Terms and conditions of the Carbon Intensity (Great Britain) API here.


unexpandviewport(3f) - [M_draw:VIEWPORT] undo expandviewport(3f) (LICENSE:PD)
Synopsis
Description
Example
subroutine unexpandviewport()
Does the reverse of expandviewport. Basically, it returns M_draw to using the largest square of the device for its viewport calculations.
Sample program:
program demo_unexpandviewport use M_draw use M_draw, only : D_BLACK, D_WHITE use M_draw, only : D_RED, D_GREEN, D_BLUE use M_draw, only : D_YELLOW, D_MAGENTA, D_CYAN implicit none integer :: ipaws!! set up graphics area call prefsize(1000,200) call vinit( ) ! start graphics using device $M_DRAW_DEVICE
!! draw circle that fills default world coordinate window call polyfill(.true.) call color(D_GREEN) call circle(0.0,0.0,1.0) ipaws=getkey() !! pause
!! set new scales for viewport so <-1,-1> and <1,1> are at !! corners of display instead of corners of largest square !! that can fit on display call expandviewport() call viewport(-1.0,1.0,-1.0,1.0)
!! draw circle that fills default world coordinate window again !! instead of a circle, the mapping now produces an ellipse unless !! this device has a square display call polyhatch(.true.) call hatchpitch(0.1) call hatchang(30.0) call linewidth(40) call color(D_CYAN) call circle(0.0,0.0,1.0) ipaws=getkey() !! pause
!! set new scales for viewport so <-1,-1> and <1,1> are at !! corners of largest square that fits on display call unexpandviewport() !! actually change to the new viewport call viewport(-1.0,1.0,-1.0,1.0)
!! now the same circle should draw where the original one did call color(D_BLACK) call hatchang(120.0) call linewidth(40) call circle(0.0,0.0,1.0) ipaws=getkey() !! pause
call vexit() !! wrap up graphics
end program demo_unexpandviewport
| Nemo Release 3.1 | unexpandviewport (3) | July 22, 2023 |


There are a lot of child malnutrition nonprofits around the globe working dilligently to fight against child hunger and undernourishment. Keeping track of all the data for thousands of kids that are screened can be a difficult and time-consuming process. THRIVE allows their data to be easily entered, saved and tracked over time, keeping records organized by both country and community name. We take care of the tedious work, so your team can dedicate more time ensuring the next generation is getting the proper nourishment needed to one day lead our world.


Yemen’s Houthis release seized S. Korean, Saudi vessels: S. Korea Foreign Ministry
The Houthis had said it would release vessels it had captured if they prove to be South Korean
by ReutersThree vessels and 16 people, including two South Korean nationals, who had been seized by Yemen's Houthi movement have been released on November 19, South Korea's Foreign Ministry said on November 20.
It consisted of two South Korean vessels and one Saudi Arabia-flagged vessel, and the families of the South Korean nationals have been notified, the ministry said in a statement.
Yemen's Iran-aligned Houthi movement had said on November 19 it would release vessels it had captured if they prove to be South Korean, after it seized a rig being towed by a Saudi tugboat in the southern Red Sea.


Brought to you by Dmytro Ilhanaiev
Overview
import { ApolloServer, gql } from 'apollo-server'
const typeDefs = gql`
type Book { title: String }
type Query { books: [Book] }`
const resolvers = {
Query: { books: () => [{ title: 'Title' }] }
}
const server = new ApolloServer({ typeDefs, resolvers })
server.listen().then(({ url }) =>
console.log(`🚀 Server ready at ${url}`))
So? What else is waiting for me during work with Apollo server?
A lot of joy, obviously
Executable schema
import { makeExecutableSchema } from 'graphql-tools';
export const schema = makeExecutableSchema({
typeDefs,
resolvers,
});
const server = new ApolloServer({
// executable schema, includes resolvers
schema,
)}
Async context
new ApolloServer({
schema,
context: async ({ req }) => {
const data = await fetch(...)
return ({
authScope: getScope(req.headers.authorization)
data
)}
},
});
Resolvers
resolveBook(root, args, context, info) {
const book = fetch(...)
return book
}
apollo-datasource-rest
Subscriptions
Apollo Engine
Not investigated well yet, but important topics and cases


LACrimBook aims to replace West's big and expensive handbook of criminal laws with a free, digital alternative. This HTML5 app runs anywhere and works whether or not you have an internet connection.
The project is open source and is offered free of charge by the Loyola Litigation and Technology Clinic under the MIT license. The source code is available on Github.
Project By:


MODEL
You can use Camunda Modeler of bpmn.io with BPE:
SESSION
Transactional persistent (rocksdb) business processing with multi-threading executioin, hierarchical traces and BPMN 2.0 XML support out of the box.
BPMN SPEC
This schema covers 80% of all workflows and provides only 20% of standard coverage. We call it Lightweight BPMN or BPE.
BPE API
BANK SAMPLE
PRESENCE
Companies that use BPE:
- pb.ua/depozit — АТ КБ "ПриватБанк"
- nynja.io — NYNJA, Inc.
- quanterall.com — Quanterall, Ltd.
- infotech.gov.ua — ДП "Інфотех".


Decentralization Is a Foreign Land
Continuing my spontaneous series here. 😁
I have been studying the decentralized space since late June 2017, so nearly four years now. If I was attending college then you could say that I am a graduate in such matters (assuming I acquired all my credits, something I failed to do in real live BUT I DIGRESS).
There’s a certain quality or metric that has seemed elusive to decentralization during that time, and that is adoption.
What’s interesting is that it seems that it is being adopted, but to me it seems that mass (or viral) adoption is not taking place.
Why?
Well, in my estimation, the decentralized mindset is so foreign. It is completely different to how you typically approach societal life that it takes a considerable adjustment – much like visiting a foreign country:
- Lost your seed words/password/keys? Tough luck. You’ve now lost access to the entire value of the wallet that stores it. 🤷♂
- Got scammed out of your entire wallet value? “Should have done your research 🤷♂” (common refrain from crypto apologists).
- Got tortured with a drill until you gave up your keys? Well, should have known what “being your own bank” really means. 🤷♂
- Someone guessed your private key? What, you didn’t have the 4-5 years to study this space to find out how it really works? 🤷♂
🤷♂ 🤷♂ 🤷♂ 🤷♂ 🤷♂
These sorts of barriers are difficult to adjust to, and are not for everyone (especially with the prominently cynical & critical attitude from the crypto community, but that’s another topic altogether). This might explain why artists can get their friends and family to onboard onto the NFT hype (adoption) but struggle getting their friends' friends and family to onboard (mass adoption).
While decentralization promotes self-responsibility and individual sovereignty, what it fails to account for is a very basic human truth, particularly in peace time: people are lazy.
Services that account for this laziness and meet the lazy where they are, are the businesses that meet the most success. Look no further than Coinbase. A company that takes a complicated, foreign matter like cryptocurrency and makes it familiar in a centralized way. It was recently valued at $100 billion when opening to the market. Sure, Coinbase is loathed by crypto “Not your keys not your crypto” purists. But do Coinbase users care?
Again, people are lazy and want others to do the work for them. (Behold, the free market to the rescue!) So what if it’s not their keys? They have taken the time to fork over their private information in adherence to KYC and AML laws. They are most likely law-abiding citizens that are simply apathetic to the technical complexity of “owning your keys.” “Not your Bitcoin, not your easy money” is more like their overarching motto.
And that is where Coinbase excels. You can own all the cryptocurrency you want and they take care of the details. Just fork over all your personally private information – you know, like you have done with many other services – and they will take care of the rest. This feels familiar and is almost basically expected, which is part of the reason it is so successful and has been valuated so highly.
That is why we have a free market, after all: to enable an efficient environment where you can create a service that improves the lives of those that use it.
All this to say that what starbeam.one offers is a vision that operates much like Coinbase: make a difficult foreign land more familiar and accessible. Whereas Coinbase specializes in trading and currencies, starbeam.one is their “NFT equivalent” (however it is our view that they are digital licenses, not NFTs.).
Currently, with the NFT space users are forced into a foreign land and if they run into problems, well, better luck next time. This is completely different from what they expect: recourse for when things go wrong. Remember, people are lazy, so when things go wrong they want someone to fix it for them – “right freaking now!”
 We simply reverse the transaction and everything was as before (assuming appropriate and authorized controls, of course). Here, we allow users to utilize a familiar system, get comfortable with it, then offer them the means to off-ramp into their foreign land once they deemed fit and are comfortable with assuming the risk of drill saw torture and other perils.
This gives the user choice and flexibility, something that simply doesn’t exist in the current market, making a foreign land a little less foreign. ✌


---
layout: default
title: Hi
---
My name's Emma. I've lived in Brooklyn since the middle of 2012, and worked in tech for as long.
Before that, I was a curator and archivist at Boston's Museum of Science. Before that, I was a college student studying history and metalsmithing.
You can find a more thorough background on my LinkedIn if you'd like.


{% extends "saas/base_dashboard.html" %} {% block saas_title %}Roles{% endblock %} {% block saas_content %} Please wait... No roles assigned yet Username Role Email [[item.user.slug]] [[item.role_description.title]] [[item.user.email]] Remove If the user couldn't be found in the system, it is a great occasion to invite them. The following invite message will be sent: Hello, I am adding to {{organization.printable_name}}. Thank you, - {{request.user.first_name}} Invite user {% endblock %}
If the user couldn't be found in the system, it is a great occasion to invite them.


“Na nūnāyaṁ paramahitānukampino,
Rahogato anuvigaṇeti sāsanaṁ;
Tathāhayaṁ viharati pākatindriyo,
Migī yathā taruṇajātikā vane”ti.
… Saṅgharakkhito thero ….
Even on retreat he doesn’t heed the counsel
of the one with supreme compassion for his welfare.
He lives with unrestrained faculties,
like a young deer in the woods.


--- layout: post status: publish published: true title: String.IsNullOrEmpty() author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 291 wordpress_url: http://winterdom.com/2005/12/stringisnullorempty date: '2005-12-18 18:48:31 +0000' date_gmt: '2005-12-18 18:48:31 +0000' categories: - ".NET" tags: [] comments: - id: 223 author: JP author_email<EMAIL_ADDRESS>author_url: '' date: '2005-12-21 22:26:43 +0000' date_gmt: '2005-12-21 22:26:43 +0000' content: |
String.Trim.IsNullOrEmpty()
- id: 224 author: Jonathan Allen author_email: '' author_url: '' date: '2006-04-04 12:43:03 +0000' date_gmt: '2006-04-04 12:43:03 +0000' content: | > String.Trim.IsNullOrEmpty() That's silly. It has be to something like String.TrimIsNullOrEmpty(x) or String.IsNullOrEmpty(x, TrimBoth) That said, it would be useful. If you don't want blanks, a single space is just as bad. - id: 225 author: Lupita Gonzalez author_email: '' author_url: '' date: '2007-07-11 07:04:35 +0000' date_gmt: '2007-07-11 07:04:35 +0000' content: | Ídolo, totalmente de acuerdo. Mine is called IsNullOrBlank. Thank you amigo. - id: 1081 author: software developer author_email<EMAIL_ADDRESS>author_url: http://www.geeks.ltd.uk/Services.html date: '2009-08-11 18:03:05 +0000' date_gmt: '2009-08-11 18:03:05 +0000' content: I use the same thing I hope they add it to frame work soon ---Steve Eichert talks about how he likes the new String.IsNullOrEmpty() in .NET 2.0. I agree it is a welcomed addition to the base libraries, and I for one I'm trying to make it a habit of using it whenever I need to do those kinds of comparisons, as it makes the code far simpler and more readable.
Now, what I'd like to see is an extended version that also Trim()s the string and checks to see if that is empty :) (how common is this for validation, huh?)


---
layout: post
title: Links of Interest
date: '2011-05-22T20:09:00.000-06:00'
author: Jay Swan
tags:
modified_time: '2011-05-22T20:09:06.963-06:00'
blogger_id: tag:blogger.com,1999:blog-5029689981158113588.post-2835006253557933834
blogger_orig_url: http://unroutable.blogspot.com/2011/05/links-of-interest.html
---
I decided to start a periodic link post, mainly to keep track of tech stuff I might want to reference again later.
HTTP Load Testing
Website Security for Webmasters
Reading List on Bayesian Methods
JavaScript PC Emulator < Run VMs in your browser?! We live in the future!
Machine Learning: A Love Story a talk by Hilary Mason


Standard feature layout
Four featured items made of an image, heading, description and meta data. They can contain a group heading
The panel background colour can be changed to soft gold, soft grey, soft blue or dark primary blue.
--- section: featured_content sub_title: Feature layouts lead: Feature layouts act as scaffolding for presenting navigation to featured content. nav_order: 2 ---
Four featured items made of an image, heading, description and meta data. They can contain a group heading
The panel background colour can be changed to soft gold, soft grey, soft blue or dark primary blue.
We rank amongst the best in the UK. Find out why we're more prestigious than many in the Russel Group.
We have a renowned personal teaching style supported by our accessible and passionate academics.
We'll equip you with essential skills to give you a competitive advantage when it comes to your career.
We're ranked one of the best universities for student satisfaction. Learn more why our students love Kent.
These feature are for giving a short teaser of the content that it links to. Keep them brief and focussed.
Keep the design looking neat and balanced by using equal amounts of content for each feature. If headings and description text vary, the visual design will look unbalanced and messy. Follow the guidelines below for suggested character limits.
You need to prepare an image to the aspect ratio of 3:2 (1024x683px).
Keep headings as short and focussed as possible. On a desktop, try avoid going on to three lines, but 1-2 lines is best, (about 55 characters).
On a desktop 2-3 lines is best (about 85 characters), 4 lines maximum.
Two featured items made of an image, heading, description and meta data.
We rank amongst the best in the UK. Find out why we're more prestigious than many in the Russel Group.
We have a renowned personal teaching style supported by our accessible and passionate academics.
Short description text.
Short description text.
Short description text.
Short description text.
Short description text.
Short description text.
Short description text.
Short description text.
We have a renowned personal teaching style.
We'll equip you with essential skills.
Primary feature with three secondary features aranged in a list.
We rank amongst the best in the UK. Find out why we're more prestigious than many in the Russel Group.
We have a renowned personal teaching style supported by our accessible and passionate academics.
We'll equip you with essential skills to give you a competitive advantage when it comes to your career.
We're ranked one of the best universities for student satisfaction. Learn more why our students love Kent.
Primary feature with four secondary features aranged in a grid.
We rank amongst the best in the UK. Find out why we're more prestigious than many in the Russel Group.
We have a renowned personal teaching style.
We'll equip you with essential skills.


These are the countries taking part in the 2018 FIFA World Cup Russia™.
The number next to each name is the ranking of that country's team in May 2018.
Based on this ranking and on the groups and match schedule, a prediction is made of the tournament's outcome.
You can alter the prediction by clicking on country names.
You can also change the ranking of a country by clicking on it, which produces an entirely new prediction.
There was a similar page for 2014 FIFA World Cup Brazil™.
The Predictorɔ source code can be seen on Kai's GitHub.


--- subject: Thank you for signing up. description: Description --- Title Content Promo Code: Subscribed
Content


Before Change
raise ValueError(f"Column {outcome} does not exist in the dataframe.")
// check that the right directions are provided
set_directions = {"up", "down", "updown", "downup"}
// linter throws an error when I use dictionary.values()
// it assumes that dictionary is a dataframe
directions_values = [value for key, value in directions.items()]
wrong_directions_provided = set(directions_values).difference(
set_directions
)
if any(wrong_directions_provided):
raise ValueError(
The direction should be a string and should be one of `up`,
`down`, `updown`, or `downup`.
)
for column, direction in directions.items():
if direction == "up":
df.loc[:, column] = df.loc[:, column].bfill(limit=limit)
elif direction == "down":
After Change
check_column(df, directions)
for _, direction in directions.items():
if direction not in {"up", "down", "updown", "downup"}:
raise ValueError(
The direction should be a string and should be one of
`up`, `down`, `updown`, or `downup`.
)
// TODO: option to specify limit per column; current implementation
// is one `limit` for all the columns. Might need refactoring, or an
// API change.
for column, direction in directions.items():
if direction == "up":
df.loc[:, column] = df.loc[:, column].bfill(limit=limit)
elif direction == "down":


VLAN Manipulation
Windows do not natively support VLAN trunking features (such as IEEE 802.1Q). Windows will identify and drop tagged frames upon receipt instead of passing to the OS.
Virtual machine guests are unable to observe trunk packets and unable to participate in or exploit VLANs as well.
Use a native Linux OS system when working with VLANs as much as possible.
Dynamic Trunking Protocol (DTP) (See also Networking / Traffic > OSI Data Link Layer > DTP)
Proprietary CISCO protocol. Negotiates a trunk port and encapsulation (802.1Q/ISL). If no trunking is performed, it defaults to an normal access port.
The DTP master shares VLAN information with all downstream switches.
As a primer, DTP will allow the switch to determine and negotiate the switchport state as a trunk port or Inter-Switch LAN (ISL is CISCO proprietary) or as an access port.
If another switch connects to a DTP port, the DTP switch will watch for the presence of 802.1Q or ISL traffic for 30 seconds. When either protocol is discovered, the DTP port will auto-configure to match the trunking configuration, sharing VLAN information with downstream switches; however, if no 802.1Q or ISL traffic is observed, then the switch will default to making the port a regular access port.
As an attacker, it can be crucial to identify if the switch port will look for and/or accept DTP traffic, forcing the switch to configure the port as a trunk port and pass all VLAN information to the attacker.
Examining the DTP protocol header frame will reveal useful information including the VLAN TRrunking Protocol domain name (if it's configured), the status of the oprt, and the DTP type.
[Yersinia]() | multi-function network attack tool focused on exploiting LAN protocols. Manipulates multiple LAN-related protocols such as STP, CDP, DTP, HSRP, DHCP, 802.1Q, VTP, ISL
See Yersinia DTP in tools section for automated DTP attack
Configuring Linux for VLAN Participation
Linux supports native 802.1Q VLAN support. Requires "8021q" kernel support and VLAN tools for the vconfig utility
Ensure you load the kernel support
# modprobe 8021q
Add the virtual interface with the specified VLAN number
# vconfig add eth0 100
Added VLAN with VID == 100 to IP -:eth0:-
# ifconfig eth0.100
<output>
Attempt to get a dhcp lease
# dhclient eth0.100
Remove the virtual adapter
# vconfig rem eth0.100
vconfig will not attempt to validate you have the correct VLAN, it will just create a VLAN sub-interface with any number you specify and encapsulate it with the appropriate header.
Can read more in the DEFCON paper: https://media.defcon.org/DEF%20CON%2024/DEF%20/CON%2024%20presentations/DEFCON-24-Bull-Matthews-Trumbull-VLAN-Hopping-ARP-MITM-in-Virtualized-UPDATED.pdf
VLAN Voice Hopping and CDP
CISCO switches accommodate a special "voice VLAN" feature. VoIP phone plugs into a switch and the PC plugs into VoIP phone. The switch must trunk two VLANs. Attackers can identify VLANs used for voice through CDP traffic.
The VoIP phone essentially becomes a 2-port switch with VLAN Trunking
Despite port configued as access, attacker can create 802.1Q trunk and gain access to the voice VLAN
CISCO Discovery Protocol (CDP)
CDP packets reveal a lot of information such as - Device ID (hostname of the switch)
- Software iOS Version
- Platform
- Port ID
- Native VLAN
- VoIP VLAN
Just like with normal VLAN hopping, you can use vconfig to add an 802.1Q VLAN to your local interface. Without the ability to directly interact with CDP the VLAN will sometimes need to be guessed or brute-forced, which is doable because there are only 4094 VLANs.
[Voiphopper](http://voiphopper.sf.net) | automates voice VLAN hopping attack including attack options for CISCO, Avaya, and Nortel switches


Let's get this out of the way to start out with: Pizza Is A Sandwhich. You may be confused at first but it is indeed an open faced sandwhich. Anyways, pizza is generally a bread base, your "crust," normally covered with a sauce, various toppings ranging from corn to pepperonis, and cheese. Once assembled, these items are placed on a pan of some sort into a hot oven, and in a few moments you can remove it, slice it up and enjoy a nice, hot sandwich.
First things first, we need our crust. These can be made from cauliflower, naan, heck even portabella mushrooms, but we're going with a basic thin crust today. Here's what you'll need.
| Name | Amount |
|---|---|
| Flour | 2 cups |
| Water (110F) | 3/4 cup |
| Yeast | 1 tsp |
| Salt | 1 1/2 tsp |
Toppings. There hang on one moment go preheat your oven to 450F before you read further here are literally more than I could ever list here because you can put nearly anything on a pizza, besides pineapple and canadian bacon together. No, Don't. So this I'll leave mostly up to your imagination, but what we basically need to have is a Sauce, which is usually tomato based but I will be using a basil pesto, our Toppings, for which I've chosen red onions and mushrooms, and some Cheese, which is most often mozzerella and parmesan, which i will be using along with some meunster. Lets go.


| DOSE: T+ 0:00 |
6.0 mg |
oral |
2C-T-2 |
(powder / crystals) |
| T+ 2:00 |
4.0 mg |
oral |
5-MeO-DiPT |
(powder / crystals) |
Generally, when I go dancing, I prefer not to be too altered. I find that I usually need just a little chemical push to put make the trance dancing space accessible, and I also don't like being paranoid, bumping into everyone, or hung over the next day.
I recently had a great experience combining two low doses of different psychedelics. I've been experimenting with the approach of using a low dose of one substance to nudge me just out of my normal state, and a low dose of another substance to slightly increase that state. Most of the time, the second drug will affect me stronger than it would have on it's own, almost as if I had taken a full dose of it, because the first substance acts as a path opener. The high that ensues is usually a subtle combination of both drugs, less overwhelming than a full dose of either, but usually enough for me to have a very good time.
The two substances in question that evening were 2c-t-2 as a path opener, and 5-meo-dipt as the added boost. At 10pm I ingested 6mg of 2c-t-2 dissolved in water. I waited for the subtle change in perception and minor anxiety (that, for me, often seems to accompany the initial phenethylamine push) to integrate and plateau, and then at midnight, I similarly took 4mg of 5-meo-dipt, just before going to a dance event.
By the time I had gotten there, relaxed, hydrated, and waited for the visiting DJ to make his appearance at the decks (1am), the 5-meo-dipt had smoothly sailed into my personal world, effortlessly combining it's own brand of intense physical energy with the mood lift that was already rolling through me from the 2c-t-2. I felt great. I danced, I shook, I let my limbs be taken over by the bass. During one rhythmic build, I had the clear sensation of having a ball of energy move up my spine in beat with the music, and explode above my head as everyone's arms raised to welcome another peak in the mix. Again, during all this, as with my other low dose experiments, I was able to stop it whenever I wanted, and coordinate myself enough to get water, talk to a few people etc...
At 4:00 am, I had had enough, and left. Home by 4:45am, was able to fall asleep an hour later. Felt energized and calm the next day.
As a side note: the days before I had been saddled with a debilitating crick in my neck that kept me from turning my head in either direction more than a few inches. When these occur with me, they usually take a week to resolve. It was my second day into this episode, when I decided to go dancing. The day after the above experience, my neck pain was completely gone! And remains so several day later. All in all, cheaper and more fun than a chiropractor!
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


For the fig-swirl: Melt butter over medium heat in a saucepan. Add brown sugar and stir to dissolve. Halve all of the figs and toss in the saucepan with water and lemon juice. Cook over medium heat, stirring frequently, until you have a chunky-jammy mixture. Add salt with one or two stirs, set aside and let cool completely.
Ice cream: In a small pot over medium heat, combine milk and granulated sugar until sugar is completely dissolved and the milk is just barely lukewarm. Whick in the egg yolks. Set mixture in the fridge and wait until the fig mixture is cooled.
Using an ice cream machine, pour liquids into the frozen basin and process according to the manufacturer instructions, i.e. let spin and thicken for 20 minutes before adding mascarpone, fig jam mixture, and the nuts. Continue to process for +/- 10 minutes. Pour semi-frozen mixture into a pyrex dish or glass tupperware. Freeze for at least two hours before serving.
Vanessa Stevenson
Food enthusiast, photography fan. Add a pinch of raw foodism and that's pretty much who I am.


During this rotation we made custom T-shirts by printing a patern or picture that we made in photoshop, we also used dye sublimation. I sublimated a pattern onto a dog bowl.


BABY BOY A film review by Annette Cardwell Copyright 2001 filmcritic.com
Ten years after releasing the groundbreaking Boyz N the Hood, writer-director John Singleton revisits his "young black man in the ghetto struggling against a violent end" cautionary tale, but with more humor and realism, in his newest film Baby Boy, showing that maybe baby boy Singleton's done some growing up himself.
In the same South Central Los Angeles neighborhood, real life is about to crash down on 20-year-old Jody (model/singer/VJ Tyrese Gibson). He has two children by two different "baby mamas" -- level-headed Yvette (Taraji Henson) and too-young Peanut (Tamara LaSeon Bass) -- he's unemployed, and he's still living with his own mama Juanita (A.J. Johnson). He's also clashing with his mom's new man, an aging "O.G." named Melvin (Ving Rhames) who's looking to move in; and, he's got to keep his volatile roughneck friend Sweetpea (Cuba Jr.'s brother Omar Gooding) out of trouble, without getting dragged down into more trouble himself. And to top things off, he's seeing competition from Yvette's ex-con ex-boyfriend (the inimitable Snoop Dogg).
While this all-too-familiar story of inner city woe has been done to death over the last decade, Singleton manages to keep it fresh and unpredictable by steering the plot in unconventional directions throughout the movie. He puts unusual yet plausible twists on such overplayed topics as drive-by shootings and domestic abuse, and still manages to deliver some powerful messages in the mix. And unlike the caricaturized roles in Boyz, he's created very complex characters that are more believably flawed. Instead of Laurence Fishburne's ultra-Zen and all-knowing father, we get Ving Rhames' reformed killer who wants to teach Jody how to be a man, but has very little patience for his hardheaded immaturity. Thankfully, Singleton seems to have gotten a better sense of humor these last few years (maybe Shaft loosened him up?) and peppers the movie with a number of hilarious scenes, including one where Jody walks in on a totally naked Melvin cooking eggs for breakfast.
It also seems that Singleton has great luck with putting non-actors to work in his movies. Just as Ice Cube showed his acting chops as Dough Boy in Boyz, Snoop really produces with a menacing role, and film newcomer Tyrese turns out a dramatic and varied starring performance. Singleton's women are even more stunning, especially Johnson as the tough and torn Juanita and Henson, who handles Yvette's roller coaster emotions with skill.
By refusing to be as dogmatic or overly simplistic as he's been with his other films, Singleton may have redeemed himself and his talent with Baby Boy. Perhaps now we can erase those bad memories of Higher Learning.

Visit filmcritic.com on the Web at http://www.filmcritic.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


--- title: About ---
This little page simply collects in one place torrent files to download the most recent versions of Linux and *BSD. It's a work in progress.
It's made with nanoc and hosted in GitHub Pages.
Source code for this project is here. I'm fmariluis.


Trader Vladimir:
Now, please tell me what I can do for you!
Buy equipment for Fighters.
Buy equipment for Wizards.


--- layout: page title: A parent: SWERC 2016 ---
|
Within Arm’s Reach
João wants to join the robotic football team of his university. However, since he knows little about robotics and mathematics, he decided to build a 2-dimensional robotic arm to bootstrap his knowledge. The robotic arm is composed of N segments of various lengths. The segments can form any angle between them, including configurations that make it appear to self-intersect when viewed from above. The robotic arm works great, but it is not trivial to position the arm’s tip as close as possible to given x, y target coordinates with so many joints to control. Can you help João? TaskGiven the robotic arm description and target coordinates relative to the arm’s origin, calculate a configuration that places the arm’s tip as close as possible to the target. InputThe first line contains N, the number of segments composing the robotic arm. N lines follow, each with an integer Li describing the length of the ith segment from the fixed point until the arm’s tip. There is one more line with 2 integers: the x, y coordinates of the target point to reach. Constraints
OutputThe output should contain N lines, each containing two real numbers xi, yi indicating the coordinates of the tip of the ith segment. The length of the ith segment computed from the solution and input Li may not differ by more than 0.01. Similarly, the absolute error between the solution’s distance to the target and the minimum possible distance to the target cannot exceed 0.01. Note that, in general, there are many solutions. Your program may output any of them. Sample Input 13 5 3 4 5 3
Sample Input 22 4 2 -8 -3 Sample Output 2-3.745 -1.404 -5.618 -2.107 Sample 2 ExplanationComputer Science Department Universidade do Porto |


| Package | Description |
|---|---|
| oracle.wsc.android.call |
Provides a Call handling API.
|
| Class and Description |
|---|
| Call
Represents a call which could have any combination of audio/video/dataChannel capabilities.
|
| Call.Observer
Observer class that the application can implement to be informed of changes in the
Call . |
| Call.TrickleIceMode
Trickle ICE modes.
|
| CallConfig
Call Configuration that describes the capabilities (audio/video/data channel) of a call.
|
| CallPackage
Package handler that enables call applications.
|
| CallPackage.Observer
Observer class for the
CallPackage to receive notifications whenever a new call arrives. |
| CallState
Represents the various states of a Call.
|
| CallUpdateEvent
Represents the various states of a Call update event.
|
| Cause
Identifies the
StatusCode and extension headers related to an event. |
| DataChannelConfig
Represents configuration for a specific data channel.
|
| DataChannelOption
Provides options for configuring the data channel.
|
| DataTransfer
Represents handle to all data channel operations.
|
| DataTransfer.DataReceiver
Entity to receive data over this data channel.
|
| DataTransfer.DataSender
Entity to send data over this data channel.
|
| DataTransfer.DataTransferState
Status of the DataTransfer object.
|
| DataTransfer.Observer
Observer for this data transfer on incoming data and state changes.
|
| MediaDirection
Direction of the local media stream.
|
| MediaStreamEvent
Media stream event enumerator.
|


{{ partial "head.html" . }}
{{ partial "nav.html" . }}
En Construction
{{ partial "contribute.html" . }} {{ partial "footer.html" . }} {{ partial "scripts.html" . }}


This is a demo project for a techtalk about modular approach to user interface design.
You will need to install dependencies before running this demo on your localhost:
npm install
Build project in order to generate distributive files:
grunt build
Run a development server to view project on your localhost:
grunt serve
To build the styleguide only, run following:
grunt styleguide


Yachts must clear through Mina Sulman, which is the main port and is reached through a 1 mile (1.5 km) long dredged channel between Sitra and Muharraq islands.
Port Control maintains permanent watch on Channel 16 and should be contacted when 3 miles offshore. Pilotage is compulsory but may be waived for small craft in daytime.
Last updated August 2015.
Most nationals will be granted a visa on arrival or can apply for a eVisa for a stay of 14 days.
For some nationals, the visa application requires a local sponsor, which can be a hotel one is booked into, and a "No Objection Certificate" from the Bahrain Immigration Office.
Fines are strictly imposed on visitors overstaying their visas.
Visas are available at all Bahrain Embassies and Consulates. Extensions are available from Department of Immigration, Manama.
There is now an online visa application site which can issue a 14 day eVisa. www.evisa.gov.bh Your elegibility for a visa and the conditions applicable can also be checked on this site.
Those with Israeli passports or passports containing an Israeli stamp will be denied entry.
Last updated August 2015.
Firearms, ammunition and all classes of explosives requires prior permission from the Ministry of he Interior and Civil Aviation Affairs of Bahrain.
Items of Israeli origin.Last updated August 2015.
Bahrain has good quality, modern medical facilities. Treatment is available to citizens and residents free, or at a nominal cost. There are also several private hospitals. Well stocked pharmacies exist all over the Kingdom.
The biggest risk to health is being knocked over by a vehicle while crossing a road.
Typhoid fever and hepatitis A occur; hepatitis B is endemic.
All pets entering Bahrain from United States, Canada and other countries with low incidence of rabies will be quarantined for minimum of 30 days. Bahrain requires that a current Import Permit be produced before entering the country. Dogs must be vaccinated against rabies, canine distemper, parvo, hepatitis and Leptospira. Cats must receive the rabies vaccine and tri-cat vaccine. The most recent of a minimum of two vaccinations for rabies must have taken place at least one month but not more than six months prior to travel.
A pet passport form can be obtained here.
Delmon Boarding Kennels Co, Kingdom of Bahrain CR, 55968 +973 17694066 - Tel +973 39717609 / mob +973 397298 can make the arrangements for the arrival of pets.


Movie ‘Frozen’ inspires atmospheric modeling classwork
BATON ROUGE, La. (AP) - The Disney movie “Frozen” inspired an assignment for a Louisiana State University class in modeling the marine atmosphere.
The question: If the sea froze while the air temperature was well above freezing, could it bring on wintry weather, as it did in the movie after Princess Elsa runs across the ocean and freezes it? The answer was that, under some circumstances, such a change would be possible.
Assistant Professor Paul Miller says he got the assignment idea after watching “Frozen” many, many, many times with his 4-year-old daughter.
He set the simulations in 1840s Norway, using climate data from a set going back to 1851 - the earliest available.
Miller says they took two lab sessions of nine scheduled this semester.
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Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


--- extends: minimal.j2 default_block: main title: Open-Source Codes description: Codes ---
I have made contributions to Torch7 deep nets library and ROS. Here are some of my codes, tutorials and contributions.
My implementation of Cooijman et. al's Recurrent Batch Normalization in Torch7. Available in Torch 7's Element Research Inc. RNN Library.
The Sorensen-Dice index measures the degree of similarity between two sample sets. Geiven targets X and Y in two sample datasets, the quotient of similarity is calculated as .
Nicely computes the Vandermonde matrix, Savitzky-Golay differentiation filters and smoothing coefficients for a noisy, sequential signal. It is a textbook implementation of the Savitzky-Golay filter algorithm following Sophocles Orfanidis treatment in his book.
Using stacked layers of neural networks, I trained multilayer perceptrons, simple recurrent neural networks, long short-term memory cells, fast LSTMS (with gates computed in one fell-swoop) to model the relationship between input and output data.
Please check out the "soft-robot" tag only. Other branches for other identification datasets are actively being developed.
This was my code entry for Kaggle's ultrasound data science challenge of August 2016. The problem is well-described on the competition page. This code was developed in Torch 7. My convolutional architecture is strongly inspired by U-Net, Alec Radford and Soumith Chintala's DcGAN and the all-convolutional net of Springenberg et al.
Code herePart of the Torch demos/tutorials
My implemention of approximate sync policies within the message filters class of ROS Image Transport communication protocol. This code subscribes to the color_rect, depth_rect and creates a point cloud visualizer from the depth and colored images.


Unsupervised text embeddings can be generalized for different tasks but they have weaker predictive powers (as compared to end-to-end trained deep learning methods) for any particular task. But the deep learning techniques are expensive and need a large amount of supervised data and a large number of parameters to tune.
The paper introduces Predictive Text Embedding (PTE) - a semi-supervised approach which learns an effective low dimensional representation using a large amount of unsupervised data and a small amount of supervised data.
The work can be extended to general information networks as well as classic techniques like MDS, Iso-map, Laplacian EigenMaps etc do not scale well for large graphs.
Further, this model can be applied to heterogeneous networks as well unlike the previous works LINE and DeepWalk which work on homogeneous networks only.
The paper proposes 3 different kinds of networks:
All 3 graphs are integrated into one heterogeneous text network.
First, the authors extend their previous work, LINE, for heterogenous bipartite text networks as explained:
Given a bipartite graph G = (VA \bigcup VB, E) , where VA and VB are disjoint set of vertices, the conditional probability of va (in set VA) being generated by vb (in set VB) is given as the softmax score between embeddings of va and vb and normalised by the sum of exponentials of dot products between vb and all nodes in VA.
| The second order proximity can be determined by the conditional distributions *p(. | vj)*p(. | vj)*. |
The objective to be minimised the KL divergence between the conditional distribution p(.\vj) and the emperical distribution p^(.\vj) (given as wi, j/degj).
Now, the 3 individual networks can all be interpreted as bipartite networks. So node representation of all the 3 individual networks is obtained as described above.
For the word-label network, since the training data is sparse, one could either train the unlabelled networks first and then the labelled network or they all could be trained jointly.
For the case of joint training, the edges are sampled from the 3 networks alternatively.
For the fine-tuning case, the edges are first sampled from the unlabelled network and then from the labelled network.
Once the word embeddings are obtained, the text embeddings may be obtained by simply averaging the word embeddings.
Baseline Models
For long documents, PTE (joint) outperforms CNN and other PTE variants and is around 10 times faster than CNN model.
For short documents, PTE (joint) does not always outperform CNN model probably because the word sense ambiguity is more relevant in the short documents.


Remember, you can not cancel deletion." %}


--- title: ‘Peak Oil’ Is a Waste of Energy layout: post ---
Like many Malthusian beliefs, peak oil theory has been promoted by a motivated group of scientists and laymen who base their conclusions on poor analyses of data and misinterpretations of technical material.


d'Arc is a small, fast, straight forward, you better read-only, sub-API interface to Lua data for both Lua 5.1.4 and LuaJIT 2 beta 7. It's not for the faint of heart but really sweet. Using it may get you fired. Or promoted. Or both, just like Jeanne.
d'Arc lets you read out Lua variables and tables as fast as possible, for both Lua and LuaJIT. Done right, i.e. using the official Lua API, you will always be wasting quite a bit of time when traversing a table, or reading out a variable. Which - life can be so confusing - implies that under some circumstance doing it right may not be right after all. Like, if performance matters. It's complicated. Jeanne to the rescue.
Using d'Arc allows you to avoid the repeat look ups, hash calculations and stack writes that bog down table traversion and variable access when using the official API for Lua or LuaJIT. And once you have accessed a variable, you keep a direct C pointer to its value, rather than going via a look up and the stack again. When traversing a table, d'Arc uses the existing, implicit pointers to the next elements rather than doing separate look ups for each and every individual key, as the official API does.
As an example, here is the implementation of d'Arc vs. the official API, at the C level. On the face of it, d'Arc looks like this:
const char *s = XLUA_STRING(tval);
... instead of going via the Lua state and stack, like this:
const char *s = lua_tostring (lua_State *L, int index);
The calls are not only different in form but as the different parameters suggest, they are used in different places. Which is what provides for the performance gain. With d'Arc you use pointers to variable structures (e.g. 'tval'), not the Lua state and stack index (L, and index).
Watch out when you get all enthusiastic and start feeling like writing to the variables. That can have all sorts of consequences that you will not have plumbed until you thought about string interning, garbage collection, longjumping, yielding and parallel processes. Stay with reading unless you know what you are doing. Which, anyway, happens to be the requirement for using d'Arc in the first place. In case that hadn't transpired yet.
Maybe memorize it like this: Jeanne listened to God and that worked great for a while. But it didn't necessarily work the other way around when she got in trouble. The quintessential read-only: God is listening. But he likes you to take care of the action.
Basically you get a bunch of oyster shucker macros for direct access of the actual contents of the VM value structures, and to test for the content types (string, number, table etc).
Plus, one function to traverse a table in low-level flight.
The macros and that fold function do not alter the Lua stack and no values are copied. You could write to and change the variable contents, but that's not the intention at this point. There are side effects, e.g. strings are interned so changing a string will change all of the same value, etc.
1) Stack Access
TValue *o = XLUA_INDEX_TO_ADDRESS(L, index);
This returns a TValue pointer of a value residing on the Lua stack. Usually, that's your starting point. As mentioned before, d'Arc does not alter the Lua stack.
If you're only starting out with Lua C programming: the Lua stack is not the C stack, it's a specific, Lua VM structure used for the values the Lua VM swishes around.
2) Type Tests
XLUA_IS_NIL(o)
XLUA_IS_TRUE(o)
XLUA_IS_FALSE(o)
XLUA_IS_BOOLEAN(o)
XLUA_IS_NUMBER(o)
XLUA_IS_STRING(o)
XLUA_IS_TABLE(o)
XLUA_IS_FUNCTION(o)
XLUA_IS_USERDATA(o)
XLUA_IS_THREAD(o)
XLUA_IS_LIGHTUSERDATA(o)
These take a TValue pointer as argument (o) and return 0 (false) or 1 (true).
3) Value Access
XLUA_BOOLEAN(o)
XLUA_NUMBER(o)
XLUA_STRING(o)
XLUA_STRING_LENGTH(o)
XLUA_TABLE(o)
These also take a TValue pointer as argument (o) and return int, int, char *, size_t, TValue respectively. Remember, Lua allows \0 as part of strings, and strings are interned.
And to say that again: the above compiles for both Lua and LuaJIT, that's the point.
4) Table Traversal
int darc_traverse(const Table *t, foldfunc fold, void *state);
This function is used to traverse a table, see the sample below. It is implemented in a fashion akin to the internal rehash functions of the VMs, which of course are highly optimized. The function applies the 'foldfunc' callback function that you define, to every table element.
typedef int (*foldfunc)(TValue *o, void *state);
The sample below will make things quite clear.
5) Table Entry Access
TValue *o = XLUA_NODE_KEYVAL(node);
TValue *o = XLUA_NODE_VAL(node);
Less frequently of interest, these functions are used on the 'nodes' that tables consist of: each node combines one key and its value. the results of these macros are TValue pointers that e.g. can be used as arguments to the type test and value access macros listed above. You may find use for these two macros, if you started decomposing and reinterpreting the darc.c source, to traverse tables yet more streamlined than by using darc_traverse(). Otherwise, you probably won't run into nodes in the beginning. But maybe once you dig deeper, so here you are.
A swearword filter, which returns true when a bad word is found in a Lua value:
> v={{'help!',{22, {'Oh fuck', 1}}, nil}
> print(filter.check(v))
true
The source is in sample/filter/filter.c.
Most of that file is boilerplate to get it to play with Lua as a library. The core of it is the following function. This is such a foldfunc function, as discussed above, which is of the type
typedef int (*foldfunc)(TValue *o, void *state);
This function is the central vehicle of the table traversal in d'Arc:
the foldfunc is passed a Lua VM value to process, TValue *o. This is a table element, and it can also be a nested table.
if you need to collect state for your work inside the table, and to return results, you can pass a data structure in using the state pointer
if foldfunc returns 0, the traversal is stopped, and at the top level, 0 is returned
In the case of the filter.c sample, you can follow how the foldfunc indirectly calls itself, by passing itself to darc_traverse as function pointer. This should be the usual way to traverse into nested tables.
int lame_word_filter(TValue *subject, void *state)
{
if(XLUA_IS_STRING(subject))
/* actually act on the string. */
return inspect(XLUA_STRING(subject));
else if(XLUA_IS_TABLE(subject))
/* traverse down into a table. */
return darc_traverse(XLUA_TABLE(subject), lame_word_filter, state);
/* ignore all other types. */
return 1;
}
Generally, traversing down into a nested table that you encounter, should be initiated in that foldfunc. Which is completely straight forward: since where you start from is usually a TValue, which could be a table, or not. Therefore, seen from the outer, Lua-registered function, you would usually start with a call to your 'foldfunc', not darc_traverse(). It's your foldfunc that then calls darc_traverse() first, if appropriate. (Or it does something else completely, e.g. in case the top value is not a table anyway.)  Are you with me? If you know a better way to put this, don't hesitate to let me know.
And, again: this source compiles for Lua and LuaJIT, even though it doesn't use the official API. It accesses the values more directly, eg. using the macros XLUA_IS_STRING(), XLUA_STRING() etc. On the other hand, the source shown here is not optimized to the hilt so as to remain instructive.
The above function lame_word_filter() is tied into Lua by this generic function:
static int filter_check(lua_State *L) {
lua_settop(L, 1);
const TValue *subject = index2addr(L, -1);
lua_pop(L, 1);
int ok = lame_word_filter(subject, (void *)0);
lua_pushboolean(L, !ok);
return 1;
}
For this sample, the integer return value is quite usefull. It suffices to report back 'yes' or 'no'. And that's what it always does, it signals if the traversal was complete (1) or broken off (0). In this case, that is used to report back whether an offensive word was found, or not. For more interesting stuff, you would use the state pointer.
Please send feedback, advise and corrections to<EMAIL_ADDRESS>or send me a message, or open an issue on github.com.
Thanks,
Henning


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Brief description
List of things to test


Preparation time
over 2 hours
Cooking time
30 mins to 1 hour
Serves
Serves 4
Crisp arancini with a gooey cheesy centre. Delicious! Cooked live at the National Festival of Making in Blackburn.
Bring the stock to the boil in a large saucepan then maintain it at a gentle simmer.
Meanwhile, heat the oil in a saucepan on a medium to low heat, add the onions and garlic and cook till soft and translucent in colour.
Add the rice to the onions and cook for 2 minutes mixing well and coating all the rice, then add the wine and simmer until evaporated. Add the hot stock to the rice mixture one ladle at a time, stirring continuously and allowing the rice to absorb the stock before adding the next ladle. Keep repeating the process until all the stock has been used up, the rice mixture should be almost cooked and thick. Stir the herbs and parmesan into the mixture. Spread the risotto onto a tray, allow to cool, then refrigerate until completely cold.
To shape the arancini, roll tablespoons of the risotto into balls, then press a piece of Taleggio into the centre of each ball and enclose the cheese in the rice completely.
Place the flour, egg and breadcrumbs into 3 separate bowls. Lightly dust each arancini in the flour, then dip into the egg and roll in the breadcrumbs until evenly coated.
Fill a deep-fat fryer or a large, deep, heavy-bottomed pan two-thirds full with the oil. Heat to 180C (a cube of bread should turn brown in one minute when dropped in.) CAUTION: Hot oil can be dangerous. Do not leave unattended.
Deep fry the arancini in batches, for 3 minutes at a time, turning constantly, until heated through and golden brown. Drain on a plate lined with kitchen paper.
Serve with grated parmesan and the lemon wedges and some baby leaves drizzled with balsamic vinegar.


Ready-made ensemble classes¶
Currently, ML-Ensemble implements provides four ready-made ensembles classes:
Each ensemble class can be built with several layers, and each layer can
output class probabilities if desired. The SequentialEnsemble
class
is a generic ensemble class that allows the user to mix types between layers,
for instance by setting the first layer to a Subsemble and the second layer
to a Super Learner. Here, we will briefly introduce ensemble specific
parameters and usage. For full documentation, see the API Reference section.
However, by accessing the low-level API it is possible
to build virtually any type of ensemble. Further ready-made classes will be
introduced on demand. Feel free to raise a feature issue at Github.
Super Learner¶
The SuperLearner
(also known as a Stacking Ensemble) is an
supervised ensemble algorithm that uses K-fold estimation to map a
training set \((X, y)\) into a prediction set \((Z, y)\),
where the predictions in \(Z\) are constructed using K-Fold splits
of \(X\) to ensure \(Z\) reflects test errors, and that applies
a user-specified meta learner to predict \(y\) from \(Z\).
The main parameter to specify is the folds
parameter that determines the
number of folds to use during cross-validation. The algorithm in
sudo code follows:
- Specify a library \(L\) of base learners
- Fit all base learners on \(X\) and store the fitted estimators.
- Split \(X\) into \(K\) folds, fit every learner in \(L\) on the training set and predict test set. Repeat until all folds have been predicted.
- Construct a matrix \(Z\) by stacking the predictions per fold.
- Fit the meta learner on \(Z\) and store the learner
The ensemble can be used for prediction by mapping a new test set \(T\) into a prediction set \(Z'\) using the learners fitted in (2), and then mapping \(Z'\) to \(y'\) using the fitted meta learner from (5).
The Super Learner does asymptotically as well as (up to a constant) an Oracle selector. For the theory behind the Super Learner, see [1] and [2] as well as references therein.
Stacking K-fold predictions to cover an entire training set is a time
consuming method and can be prohibitively costly for large datasets.
With large data, other ensembles that fits an ensemble on subsets
can achieve similar performance at a fraction of the training time.
However, when data is noisy or of high variance,
the SuperLearner
ensure all information is
used during fitting.
References¶
| [1] | van der Laan, Mark J.; Polley, Eric C.; and Hubbard, Alan E., “Super Learner” (July 2007). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 222. http://biostats.bepress.com/ucbbiostat/paper222 |
| [2] | Polley, Eric C. and van der Laan, Mark J., “Super Learner In Prediction” (May 2010). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 266. http://biostats.bepress.com/ucbbiostat/paper266 |
Notes¶
This implementation uses the agnostic meta learner approach, where the user supplies the meta learner to be used. For the original Super Learner algorithm (i.e. learn the best linear combination of the base learners), the user can specify a linear regression as the meta learner.
Subsemble¶
Subsemble
is a supervised ensemble algorithm that uses subsets of
the full data to fit a layer, and within each subset K-fold estimation
to map a training set \((X, y)\) into a prediction set \((Z, y)\),
where \(Z\) is a matrix of prediction from each estimator on each
subset (thus of shape [n_samples, (partitions * n_estimators)]
).
\(Z\) is constructed using K-Fold splits of each partition of X to
ensure \(Z\) reflects test errors within each partition. A final
user-specified meta learner is fitted to the final ensemble layer’s
prediction, to learn the best combination of subset-specific estimator
predictions.
The main parameters to consider is the number of partitions
, which will
increase the number of estimators in the layer by a factor of the number of
base learners specified, and the number of folds
to be used during
cross validation in each partition.
The algorithm in sudo code follows:
For each layer in the ensemble, do:
Specify a library of \(L\) base learners
Specify a partition strategy and partition \(X\) into \(J\) subsets.
For each partition do:
- Fit all base learners and store them
- Create \(K\) folds
- For each fold, do:
- Fit all base learners on the training folds
- Collect all test folds, across partitions, and predict.
Assemble a cross-validated prediction matrix \(Z \in \mathbb{R}^{(n \times (L \times J))}\) by stacking predictions made in the cross-validation step.
Fit the meta learner on \(Z\) and store the learner.
The ensemble can be used for prediction by mapping a new test set \(T\) into a prediction set \(Z'\) using the learners fitted in (1.3.1), and then using \(Z'\) to generate final predictions through the fitted meta learner from (2).
The Subsemble does asymptotically as well as (up to a constant) the Oracle selector. For the theory behind the Subsemble, see [3] and references therein.
By partitioning the data into subset and fitting on those, a Subsemble can reduce training time considerably if estimators does not scale linearly. Moreover, Subsemble allows estimators to learn different patterns from each subset, and so can improve the overall performance by achieving a tighter fit on each subset. Since all observations in the training set are predicted, no information is lost between layers.
References¶
| [3] | Sapp, S., van der Laan, M. J., & Canny, J. (2014). Subsemble: an ensemble method for combining subset-specific algorithm fits. Journal of Applied Statistics, 41(6), 1247-1259. http://doi.org/10.1080/02664763.2013.864263 |
Notes¶
This implementation splits X into partitions sequentially, i.e. without
randomizing indices. To achieve randomized partitioning, set shuffle
to True
. Supervised partitioning is under development.
Blend Ensemble¶
The BlendEnsemble
is a supervised ensemble closely related to
the SuperLearner
. It differs in that to estimate the prediction
matrix Z used by the meta learner, it uses a subset of the data to predict
its complement, and the meta learner is fitted on those predictions.
The user must specify how much of the data should be used to train the layer,
test_size
, and how much should be held out for prediction. Prediction for
the held-out set are passed to the next layer or meta estimator, so information
is with each layer.
By only fitting every base learner once on a subset of the full training data,
BlendEnsemble
is a fast ensemble that can handle very large datasets
simply by only using portion of it at each stage. The cost of this approach is
that information is thrown out at each stage, as one layer will not see the
training data used by the previous layer.
With large data that can be expected to satisfy an i.i.d. assumption, the
BlendEnsemble
can achieve similar performance to more
sophisticated ensembles at a fraction of the training time. However, with
data data is not uniformly distributed or exhibits high variance the
BlendEnsemble
can be a poor choice as information is lost at
each stage of fitting.
Temporal Ensemble¶
The TemporalEnsemble
class is similar to the SuperLearner
, but
differs in that it uses a time series cross-validation
strategy to create training and test folds that preserve temporal
ordering in the data. The cross validation strategy is unrolled
through time. For instance:
| fold | train obs | test obs |
|---|---|---|
| 0 | 0, 1, 2, 3 | 4 |
| 1 | 0, 1, 2, 3, 4 | 5 |
| 2 | 0, 1, 2, 3, 4, 5 | 6 |
Different estimators in the ensemble can operate on different time scales, allow efficient combinations of different temporal patterns in one model.
Sequential Ensemble¶
The SequentialEnsemble
allows users to build ensembles with
different classes of layers. Instead of setting parameters upfront during
instantiation, the user specified parameters for each layer when calling
add
. The user must thus specify what type of layer is being added
(blend, super learner, subsemble, temporal), estimators, preprocessing if applicable,
and any layer-specific parameters. The Sequential ensemble is best illustrated
through an example:
>>> from mlens.ensemble import SequentialEnsemble
>>> from mlens.metrics.metrics import rmse
>>> from sklearn.datasets import load_boston
>>> from sklearn.linear_model import Lasso
>>> from sklearn.svm import SVR
>>> from pandas import DataFrame
>>>
>>> X, y = load_boston(True)
>>>
>>> ensemble = SequentialEnsemble(scorer=rmse)
>>>
>>> # Add a subsemble with 10 partitions and 10 folds as first layer
>>> ensemble.add('subsemble', [SVR(), Lasso()], partitions=10, folds=10)
>>>
>>> # Add a super learner with 20 folds as second layer
>>> ensemble.add('stack', [SVR(), Lasso()], folds=20)
>>>
>>> # Specify a meta estimator
>>> ensemble.add_meta(SVR())
>>>
>>> ensemble.fit(X, y)
>>>
>>> ensemble.data
score-m score-s ft-m ft-s pt-m pt-s
layer-1 lasso 0 11.79 2.74 0.00 0.00 0.00 0.00
layer-1 lasso 1 7.53 1.24 0.00 0.00 0.00 0.00
layer-1 lasso 2 7.24 1.82 0.00 0.00 0.00 0.00
layer-1 lasso 3 9.59 1.72 0.01 0.00 0.00 0.00
layer-1 lasso 4 12.44 3.48 0.00 0.00 0.00 0.00
layer-1 lasso 5 17.36 2.65 0.00 0.00 0.00 0.00
layer-1 lasso 6 8.89 1.81 0.00 0.00 0.00 0.00
layer-1 lasso 7 12.72 3.52 0.00 0.00 0.00 0.00
layer-1 lasso 8 12.18 1.23 0.00 0.00 0.00 0.00
layer-1 lasso 9 7.27 1.82 0.00 0.00 0.00 0.00
layer-1 svr 0 9.62 1.19 0.00 0.00 0.00 0.00
layer-1 svr 1 9.16 0.90 0.00 0.00 0.00 0.00
layer-1 svr 2 9.97 1.36 0.00 0.00 0.00 0.00
layer-1 svr 3 11.89 0.88 0.00 0.00 0.00 0.00
layer-1 svr 4 9.37 0.77 0.00 0.00 0.00 0.00
layer-1 svr 5 11.92 1.22 0.00 0.00 0.00 0.00
layer-1 svr 6 9.23 1.03 0.00 0.00 0.00 0.00
layer-1 svr 7 12.75 1.76 0.00 0.00 0.00 0.00
layer-1 svr 8 12.88 1.67 0.00 0.00 0.00 0.00
layer-1 svr 9 9.56 1.21 0.00 0.00 0.00 0.00
layer-2 lasso 0 5.66 2.44 0.02 0.03 0.00 0.00
layer-2 svr 0 8.34 4.10 0.03 0.01 0.00 0.00
Note how each of the two base learners specified are duplicated to each of the 10 partitions.


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WITNESS LIST Transportation Committee March 22, 2005-8:00A HB 98 Against: DeMontrond, George (Self) Hall, Jr., Anthony W. (Mayor Bill White & City of Houston) HB 183 For: Douglass, Susan (University Health System & Safe Kids Coalition, San Antonio) Edwards, Carmen (Self) Gaylor, Tom (Texas Municipal Police Assoc.) O'Ryan, Anne (AAA Texas) Slay, Karen (Texas PTA) Tips, RN, Gaylen (Emergency Nurses Assn. & Brackenridge Hospital Trauma Svcs.) On: Maxson, MD, Todd (Texas Child Occupant Protection Safety Advocates) HB 1255 For: Elfant, Bruce (Self) HB 1481 For: Connealy, Chris (Self) Gutierrez, Bobby (Self and Williamson County Constable) Wilson, James R. (Williamson County Sheriff's Office) HB 1546 For: Olson, Ron (Union Pacific Railroad) Wolff, Judge Nelson W. (Bexar County Commissioners Court) On: Nichols, Robert (Tx Transportation Commission) HB 1735 For: Stallings, Robin (Texas Bicycle Coalition) HB 1815 For: Betts, Charles (Downtown Austin Alliance) Watson, Kirk (Greater Austin Chamber of Commerce) HB 1882 For: Levine, Marc S. (United DC Inc. & American Plastics Council) Underwood, Rudy (American Plastics Council) Against: Couch, Richard (Osprey Line) Hinojosa, Walter (Texas AFL-CIO) Kearns, Dennis (BNSF Railway & Texas Railroad Association) Lyons, Lt. Steve (Houston Police Dept) Milburn, Jeff (West Gulf Maritime Association & Jeff Milburn Engineering) Pichinson, Michael (Texas Conference of Urban Counties) Rasmussen, Kathy L. (APM Terminals) Stewart, Steve (Gulf Winds International, Inc &Texas Motor Truckers Assoc (Member)) On: Davis, Carol (Tx Dept of Transportation) HB 2660 For: Allen, Mike (McAllen Foreign Trade Zone & McAllen Economic Development Corp) Hentges, Sandy (Greater Austin Chamber) Olson, Ron (Union Pacific Railroad) Roby, John (Port of Beaumont & Texas Ports Association) Watson, Kirk (Greater Austin Chamber) On: Nichols, Robert (Tx Trans Commission) HJR 54 For: Olson, Ron (Union Pacific RR) On: Nichols, Robert (Tx Trans Commission) HJR 81 For: Olson, Ron (Union Pacific Railroad) On: Nichols, Robert (Tx Trans Commission)


Great engineering matters, but work ethics and professionalism
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No time sharing between multiple projects. You can directly interact with the team you're working with. We follow an open-office approach. Come anytime, talk anytime.
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Cross learnings from 10+ years of working with a variety of startups. We not only help you build the product but also give you strategic advice on how to make it and sell it better.
We'd love to hear more about your product. Let's talk and turn your great idea into something even greater!
Contact UsOur work page contains detailed case studies on all the key projects we have done so far. Have a look!
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Amazon Aurora
Offering
MySQL- and PostgreSQL-compatible enterprise-class database,
starting at
<$1/day.
Features
Up to 5 times the throughput of MySQL and 3 times the throughput of PostgreSQL
Up to 64TB of auto-scaling SSD storage
6-way replication across three Availability Zones
Up to 15 Read Replicas with sub-10ms replica lag
Automatic monitoring and failover in less than 30 seconds
Regions
all AWS Regions
Backup & Recovery
Automatic, Continuous, Incremental Backups and Point-in-time Restore
Engines
mysql: compatible with MySQL 5.6.10a
postgresql: compatible with PostgreSQL 9.6.2
RDS myqsl / postgresql
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Features
Instances offer up to 32 vCPUs and 244 GiB Memory
Supports database size up to 6 TB
Supports cross-region read replicas
Multi-AZ & PIOPS for productive enviroments
Regions
all AWS Regions
Backup & Recovery
Automatic, Continuous, Incremental Backups and Point-in-time Restore
Engines
mysql 5.5.46 - 5.7.17
postgresql 9.3.12 R1 - 9.6.3 R1
BYOL not possible
Hints
Reserved Instances available (cost reduction)
start / stop of RDS mysql possible (restart after 7 days)


Victor Osimhen reportedly emerges as target for Jose Mourinho at Tottenham
Victor Osimhen who currently plays for French club Lille has reportedly emerged as a target for newly appointed Tottenham manager Jose Mourinho.
Jose Mourinho replaced Mauricio Pochettino at Tottenham on Wednesday, November 20, and the Portuguese has started preparation for his first game against West Ham United.
The former Chelsea and Manchester United manager has been given the mandate to save the club from going down this season as they currently occupy 14th position on the standings.
And according to the report by Jeunes Footeux, Jose Mourinho is ready to table an impressive deal for Lille when the January transfer window opens to lure Osimhen to England.
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Mourinho believes the 20-year-old Nigeria striker can support Harry Kane upfront in his squad next year.
Victor Osimhen has been impressive this season for Lille in all competitions scoring seven goals in the League as well as scoring against Chelsea in the Champions League.
Iwobi opens up on what Everton manager did to improve his game against West Ham
He has also been superb for Super Eagles since Odion Ighalo retired from the national team after the 2019 African Cup of Nations in Egypt.
Meanwhile, Lille might not want to sell Victor Osimhen in January 2019 considering how superb he has been for them this season.
Earlier, Legit.ng had reported how Nigeria's Super Eagles cruised to 4-2 win over Lesotho in their 2021 Africa Cup of Nations qualifier at the Setsoto Stadium in which Osimhen scored brace.
The three-time African kings got the game started on a flat-foot as they allowed the host to take the lead in the 11th minute through a superb header from Masoabi Nkoto.
Abu Azeez: Meet the only Nigerian rated among top 50 beach soccer players worldwide | Legit TV


Class: ActiveRecord::ConnectionAdapters::PostgreSQL::Column
Do not use. This class is for internal use only.
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
|
|
|
Instance Chain:
|
|
| Inherits: |
ActiveRecord::ConnectionAdapters::Column
|
| Defined in: | activerecord/lib/active_record/connection_adapters/postgresql/column.rb |
Class Method Summary
- .new(serial: nil, generated: nil) ⇒ Column constructor
::ActiveRecord::ConnectionAdapters::Column
- Inherited
| .new | Instantiates a new column in the table. |
Instance Attribute Summary
- #array (also: #array?) readonly
-
#array?
readonly
Alias for #array.
-
#auto_incremented_by_db?
readonly
Alias for #serial?.
- #enum? ⇒ Boolean readonly
- #fmod readonly
- #has_default? ⇒ Boolean readonly
- #oid readonly
- #serial? ⇒ Boolean (also: #auto_incremented_by_db?) readonly
- #virtual? ⇒ Boolean readonly
::ActiveRecord::ConnectionAdapters::Column
- Inherited
| #limit, #precision, #scale, #sql_type, #type, | |
| #auto_incremented_by_db? | whether the column is auto-populated by the database using a sequence. |
| #auto_populated?, #bigint?, #collation, #comment, #default, #default_function, #has_default?, #name, #null, #sql_type_metadata, #virtual? |
Instance Method Summary
- #==(other) (also: #eql?)
- #encode_with(coder)
-
#eql?(other)
Alias for #==.
- #hash
- #init_with(coder)
- #sql_type
::ActiveRecord::ConnectionAdapters::Column
- Inherited
| #==, #encode_with, | |
| #eql? | Alias for Column#==. |
| #hash, | |
| #human_name | Returns the human name of the column name. |
| #init_with, #deduplicated |
::ActiveRecord::ConnectionAdapters::Deduplicable
- Included
| #-@ | Alias for Deduplicable#deduplicate. |
| #deduplicate, #deduplicated |
Constructor Details
.new(serial: nil, generated: nil) ⇒ Column
# File 'activerecord/lib/active_record/connection_adapters/postgresql/column.rb', line 9
def initialize(*, serial: nil, generated: nil, **) super @serial = serial @generated = generated end
Instance Attribute Details
#array (readonly) Also known as: #array?
[ GitHub ]# File 'activerecord/lib/active_record/connection_adapters/postgresql/column.rb', line 29
def array .sql_type.end_with?("[]") end
#array? (readonly)
Alias for #array.
# File 'activerecord/lib/active_record/connection_adapters/postgresql/column.rb', line 32
alias :array? :array
#auto_incremented_by_db? (readonly)
Alias for #serial?.
# File 'activerecord/lib/active_record/connection_adapters/postgresql/column.rb', line 18
alias_method :auto_incremented_by_db?, :serial?
#enum? ⇒ Boolean
(readonly)
[ GitHub ]
# File 'activerecord/lib/active_record/connection_adapters/postgresql/column.rb', line 34
def enum? type == :enum end
#fmod (readonly)
[ GitHub ]# File 'activerecord/lib/active_record/connection_adapters/postgresql/column.rb', line 7
delegate :oid, :fmod, to: :
#has_default? ⇒ Boolean
(readonly)
[ GitHub ]
# File 'activerecord/lib/active_record/connection_adapters/postgresql/column.rb', line 25
def has_default? super && !virtual? end
#oid (readonly)
[ GitHub ]# File 'activerecord/lib/active_record/connection_adapters/postgresql/column.rb', line 7
delegate :oid, :fmod, to: :
#serial? ⇒ Boolean
(readonly)
Also known as: #auto_incremented_by_db?
[ GitHub ]
# File 'activerecord/lib/active_record/connection_adapters/postgresql/column.rb', line 15
def serial? @serial end
#virtual? ⇒ Boolean
(readonly)
[ GitHub ]
# File 'activerecord/lib/active_record/connection_adapters/postgresql/column.rb', line 20
def virtual? # We assume every generated column is virtual, no matter the concrete type @generated.present? end
Instance Method Details
#==(other) Also known as: #eql?
[ GitHub ]#encode_with(coder)
[ GitHub ]# File 'activerecord/lib/active_record/connection_adapters/postgresql/column.rb', line 48
def encode_with(coder) coder["serial"] = @serial coder["generated"] = @generated super end
#eql?(other)
Alias for #==.
# File 'activerecord/lib/active_record/connection_adapters/postgresql/column.rb', line 59
alias :eql? :==
#hash
[ GitHub ]# File 'activerecord/lib/active_record/connection_adapters/postgresql/column.rb', line 61
def hash Column.hash ^ super.hash ^ serial?.hash end
#init_with(coder)
[ GitHub ]# File 'activerecord/lib/active_record/connection_adapters/postgresql/column.rb', line 42
def init_with(coder) @serial = coder["serial"] @generated = coder["generated"] super end
#sql_type
[ GitHub ]# File 'activerecord/lib/active_record/connection_adapters/postgresql/column.rb', line 38
def sql_type super.delete_suffix("[]") end


1) Start 30s Timer
2) Input number of sit-to-stand events below.
3) Start 30s Timer
4) Input number of arm curls
5) Start 2 minute Timer
6) Input number of leg lifts that reach target.
7) Input Flexibility Score
8) Start Timer
9) Stop Timer
10) Input Flexibility Score


Experts suggest there are two types of users who succumb to Ativan addiction. They are:
The usual methods of obtaining Ativan are no different from other prescription narcotics or opioids. They are either legally prescribed and bought through a pharmacy, or obtained through illegal drug sales and/or theft.
In the realm of theft, Ativan is usually stolen from a family member who already uses the drug, or in other cases, extended family members as well as friends.
Ativan is an anti-anxiety drug. Once taken, the drug provides a calming, euphoric feeling. Muscle relaxation and drowsiness can also occur from Ativan.
Of course, due to the relaxation and sensations of drowsiness given off by Ativan, the drug can be very dangerous when operating machinery, or in situations in which alertness and clear-minded decisions are needed.
Combining Ativan with other drugs causes an even more intense “high” than if taken alone. However, at the same time, Ativan is clinically combined with other medications to counteract their effects. Some of the more typical drugs Ativan are combined with include:
Taking Ativan in combination with other drugs is very dangerous as it increases the risk of overdose. In some cases, excessive sedation from mixing drugs can lead to unconsciousness, coma or death.
Ativan helps balance chemicals in the brain that can cause anxiety. When taken in especially large doses, Ativan binds to special receptors in the brain to produce a fleeting, intense high, followed by a prolonged state of calm.
Because Ativan is very potent and can seem harmless as a prescription drug, it is a prime candidate for both accidental and intentional abuse, as well as accidental overdose. Most commonly, overdoses occur when Ativan is taken in combination with alcohol or other drugs.
Signs of an Ativan overdose can include:
If you know someone who has an Ativan addiction, help him or her find a legitimate and accredited detox and recovery center that offers both in and outpatient services and 24-7 help, guidance and support.
Act quickly. Every moment your loved one or friend is addicted to Ativan and its inherent ability to cause addiction and eventual overdose, their lives are in danger.


| set_here {here} | R Documentation |
set_here(path = ".", verbose = TRUE)
path |
|
verbose |
|
set_here()
creates an empty file named .here
, by default
in the current directory.
When here encounters such a file, it uses the
directory that contains this file as root.
This is useful if none of the default criteria apply.
You need to restart the R session so that here()
picks up the newly created file.
set_here()
should no longer be necessary
if all scripts and reports in a project call i_am()
,
which is now the recommended workflow for declaring the project root.


Buffalo Plains [at68], plain, in Imperia, peasants: 118
Last updated Turn #106
Buffalo Plains [at68], plain, in Imperia, peasants: 118
------------------------------------------------------------------------
15: Stefano [t8e], Imperial Empire, the High Priest of the Church of
15: Halon [157], with ten angels, 45 nazgul, arrived from the west,
15: accompanied by:
15: Lord Rodrigo Bane [c8b], Imperial Empire, Bishop of the Church of
15: Dol [153], with five angels, 40 hvy. xbowmen, 14 nazgul
16: Stefano [t8e], Imperial Empire, the High Priest of the Church of
16: Halon [157], with ten angels, 45 nazgul, went east, accompanied by:
16: Lord Rodrigo Bane [c8b], Imperial Empire, Bishop of the Church of
16: Dol [153], with five angels, 40 hvy. xbowmen, 14 nazgul
18: Stack of nazgul [hx3k] attacks Stefano [t8e]!
18:
18: Stack of nazgul [hx3k], nazgul, number: 4
18:
18: Stefano [t8e], Imperial Empire, the High Priest of the Church of
18: Halon [157], with ten angels, 45 nazgul, accompanied by:
18: Lord Rodrigo Bane [c8b], Imperial Empire, Bishop of the Church
18: of Dol [153], behind 3, with five angels, 40 hvy. xbowmen,
18: 15 nazgul
18:
18: Initial missile round for the defense (Stefano [t8e]):
18: Three missiles hit!
18: Stack of nazgul [hx3k] loses three nazgul [55].
18:
18: Combat round: one
18: Missile phase:
18: Stefano's forces hit with three missiles.
18: Stack of nazgul [hx3k] suffers a hit!
18:
18: Stefano [t8e] is victorious!
18:
18: Stack of nazgul lost three nazgul.
18: Stack of nazgul [hx3k] was killed.
18:
28: Band of thieves [jg8d], bandits, number: 13, went north.
28: Tom Sen [p0u], refugee, arrived from the south.
30: The population grows by one peasant.
Routes leaving Buffalo Plains:
North, mountain, to Imperial Mountains [as68], 10 days
East, plain, to Buffalo Plains [at69], 7 days
South, desert, to Painted Desert [au68], 8 days
West, plain, to Buffalo Plains [at67], 7 days
Inner locations:
Poppy field [ae2f], poppy field, 1 day, owner:
Rat [hs5x], rats, number: 4
Seen here:
Band of thieves [jg8d], bandits, number: 13
Tom Sen [p0u], refugee